Volume 5 Supplement 1

8th German Conference on Chemoinformatics: 26 CIC-Workshop

Open Access

Using machine learning techniques for rationalising phenotypic readouts from a rat sleeping model

  • Georgios Drakakis1,
  • Alexios Koutsoukas1,
  • Suzanne Clare Brewerton2,
  • David DE Evans2 and
  • Andreas Bender1Email author
Journal of Cheminformatics20135(Suppl 1):P34

DOI: 10.1186/1758-2946-5-S1-P34

Published: 22 March 2013

Understanding the mode of action of small molecules is critical for drug research, both with respect to efficacy and anticipated side effects. Given that many compounds act on multiple targets simultaneously, it appears that linking single targets to outcomes is no longer sufficient. Hence, in this work we explore machine learning methods for rationalising phenotypic readouts from a rat model for hypnotics based on a polypharmacology approach. We hypothesise that by combining target prediction and machine learning techniques we are able to derive information regarding the mode of action of small molecules. In particular, we applied this hypothesis on a subset of the Eli Lilly SCORE™ dataset. This comprised 845 data instances, each consisting of 7 phenotypic readouts which attribute towards a good sleeping pattern. We employed a target prediction tool[1] to anticipate bioactivities of ligands in combination with the CN2 and C4.5 machine learning algorithms to derive interpretable rule lists and classification trees for the observed phenotypes. A review of the known mechanisms of action for the largest categories of hypnotics suggests that our results are in most cases consistent with current literature on the mode of action of hypnotics. This suggests that our method can potentially yield significant information regarding the mode of action of hypnotics, and in addition novel targets that are not yet well-established in literature. As further applications of this work, we are currently preparing to apply our methodology to a subset of a Traditional Chinese Medicine dataset and a phenotypic screening dataset for Xenopus.

Authors’ Affiliations

Unilever Centre for Molecular informatics, Department of Chemistry, University of Cambridge
Eli Lilly and Company Limited, Erl Wood Manor


  1. Koutsoukas A, et al: In silico target predictions: defining a benchmarking dataset and comparison of the performance of the multiclass Naïve Bayes and Parzen-Rosenblatt Window methods. submitted. 2012Google Scholar


© Drakakis et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.